By Martin H. Morrissette, Sirocco CMO – Book time with me
AI-powered agents, or “Agentic AI,” are transforming how organisations interact with customers, making processes smarter, faster, and more personalised. However, navigating the different pricing models can be challenging, as no single standard has yet emerged. Leading providers like Salesforce, Microsoft, Google, HubSpot, and AWS each take a unique approach, creating a landscape that requires careful evaluation to find the right fit for your needs. Here’s how you can make sense of it all.
First things first – do you need to pay for Agentic AI, or will “normal AI” do?
Before diving into the complexities of Agentic AI and its pricing, it might be worthwhile to assess whether your organisation truly needs this advanced solution right away or if more straightforward AI models, such as predictive or generative AI, can meet your needs.
Agentic AI is designed to handle tasks that require deep integration with existing systems, complex decision-making, or interactions that mimic human behaviour. If your business needs advanced features like sentiment analysis, conversational agents, or intelligent automation, Agentic AI may be the right choice. However, these systems often come with higher costs and more extensive development requirements. On the other hand, if your needs are more basic, such as forecasting trends, predicting customer behaviour, or generating content, predictive or generative AI may suffice. These models are often quicker to implement and can be more cost-effective, especially if you’re focused on specific outcomes like improving sales forecasting or automating content creation.
When assessing your AI needs, consider what tasks you want to automate—whether simple ones like answering FAQs or more complex tasks, such as personalising customer journeys. Think about the level of integration required: Does the AI need to connect with multiple platforms or handle real-time decision-making? Also, reflect on the long-term vision for AI in your business. Is it a core part of your strategy, or more of a short-term solution? The trade-off often comes down to cost versus complexity. Predictive and generative AI can offer quick wins with lower upfront costs, but may lack the deeper integration and customisation that Agentic AI provides. If your business is scaling rapidly or has specific needs, investing in Agentic AI could deliver significant long-term benefits.
Ultimately, the choice between Agentic AI and other forms of AI will depend on your organisation’s goals, resources, and existing tech stack. For some businesses, starting with simpler AI solutions and transitioning to more complex systems as needs grow may be the most efficient path. For others, implementing a comprehensive Agentic AI system from the start can help drive more impactful, long-term results. Whatever path you choose, it’s important to evaluate the broader landscape of AI solutions and how they align with your objectives. The key considerations discussed so far will guide you in making an informed decision, ensuring that the solution you adopt not only meets your immediate needs but also scales with your business as it evolves.
Let’s now explore the emerging trends, pricing models, and factors influencing Agentic AI costs to give you a clearer picture of how to move forward.
What influences Agentic AI costs?
Now, if you read this far, and if you’re considering Agentic AI as part of your future strategy, it’s important to get a clear picture of the factors that affect its pricing. Understanding these elements will help you better manage costs, set expectations, and ensure you’re making the right decisions for your business as you look ahead.
First, you’ll want to consider the deployment model. Most AI platforms offer cloud-based solutions, which are scalable and allow your business to grow without the burden of heavy upfront costs. However, this flexibility comes with recurring fees, often tied to usage or subscription rates, which means your costs will evolve as your usage increases.
Another factor is agent complexity. If your AI agents are limited to handling basic tasks like answering frequently asked questions, the costs will be lower. However, as you move toward more sophisticated solutions (such as systems capable of sentiment analysis or predictive modelling) the development and ongoing maintenance costs can rise significantly. It’s important to assess how complex you need your AI agents to be before making a decision.
Training and development also play a role in costs. The more tailored the training for your specific business needs, the more resources and time it takes. Customised training models are often more expensive, but they allow your AI agents to perform at their best, handling unique tasks and challenges specific to your operations.
Finally, you should consider usage patterns. The way you plan to use AI in your business will directly impact your costs. Many AI providers link pricing to the volume of interactions or users. Some charge per conversation, while others operate on subscription or pay-as-you-go models. The pricing structure you choose will depend on how frequently you anticipate using AI agents and the scale at which you expect them to operate.
Understanding these factors is essential, especially considering that the Agentic AI industry is still in its early stages. Vendors are actively exploring various monetization strategies to align with customer needs and usage patterns. For instance, Microsoft and Google have recently integrated AI features into their productivity suites, adjusting pricing models to distribute AI costs across their user bases (with mixed reactions from their users). This approach reflects a broader trend where companies are experimenting with different pricing structures to find the most effective way to offer AI solutions. As the market matures, these strategies will continue to evolve, aiming to balance accessibility with profitability.
Comparing leading pricing models
Here’s a closer look at how major vendors structure their pricing as of January 2025 (check in with your AE to make sure they are still accurate):
| Vendor | Platform | Pricing Model | Highlights |
|---|---|---|---|
| Salesforce | Agentforce | Consumption-based ($2 per conversation) | Predictable costs but can become expensive with high interaction volumes. |
| Microsoft | Copilot | Pay-as-you-go ($0.01 per message) or pre-paid packs ($200 for 25,000 messages/month) | Flexible for varying usage patterns. |
| Agentspace | Per-user model (e.g. $50/user/month) | Ideal for scaling across internal teams; higher tiers include advanced features. | |
| HubSpot | Breeze | Subscription-based (starting at $800/month) | Designed for small to mid-sized businesses with scalable options. |
| AWS | Amazon Bedrock | Pay-as-you-go (based on compute and storage) | Cost aligns with activity; expenses may fluctuate with peak demand periods. |
(Please note those solutions tend to change names quite a bit)
Leveraging your existing tech stack
The tech stack you already use plays a crucial role in determining which Agentic AI solution is the best fit for your business. If you’re already using Salesforce, for example, its native AI solutions like Agentforce might offer the smoothest path forward, with fewer integration challenges. This can save you time, effort, and resources, allowing your team to focus on more strategic initiatives.
For those already invested in Microsoft, Google, or HubSpot, choosing AI agents from these providers is often the most logical move. These solutions are designed to integrate seamlessly with their respective platforms, streamlining both setup and ongoing management. By sticking with familiar technologies, you can minimise the complexities and costs associated with third-party integrations. Ultimately, choosing an AI solution that aligns with your existing tech stack can lead to quicker implementation, greater efficiency, and a smoother transition, helping you unlock value faster.
Building your own vs. choosing established vendors
While it is possible to build your own AI agents, this route can be costly and time-consuming. Building custom agents requires investment in development, ongoing training, and maintenance. For most businesses, opting for established vendors like Salesforce, Microsoft, Google, or HubSpot is the more efficient and reliable option. These vendors offer robust AI solutions that are continually updated and come with support, making them a lower-risk and potentially more cost-effective choice.
As the market for AI agents grows, another trend to watch maybe, is the increasing availability of these solutions on marketplaces like Salesforce’s AppExchange. These platforms allow organisations like yours to access ready-made, customisable AI agents, providing a quicker (and potentially cheaper) route to deploying AI. As demand for Agentic AI continues to rise, we can expect even more options to become available on these platforms, simplifying the selection process for businesses.
Addressing customer expectations and the ROI challenge
As AI becomes increasingly common (thanks in part to free tools like ChatGPT) it’s understandable that many users may view AI as a commodity. This creates a unique challenge for businesses offering premium, enterprise-grade AI solutions: how do you demonstrate the added value and justify the investment?
Unlike free alternatives, Agentic AI platforms offer much more than basic functionality. They come with robust integration capabilities, enterprise-level security, and customisation options that can be tailored to specific industries or workflows. These benefits, while often translating to a higher price point, are essential for organisations looking to implement AI at scale, with greater control and reliability. To build trust and encourage adoption, businesses need to communicate these advantages clearly and effectively, ensuring potential customers understand the value they’re getting.
But it doesn’t stop there. The ROI of Agentic AI is still a challenge for many organisations to measure. Gartner reports that only 54% of AI projects progress from pilot to production, largely due to unclear outcomes or an inability to quantify success. However, for those who make it past the pilot phase, the rewards are substantial. Research from Microsoft and IDC found that businesses realised a 3.5x return on their AI investments, with some even achieving up to 8x. Still, it’s not always a smooth ride. A KPMG survey revealed that nearly 68% of business leaders feel pressure from investors to demonstrate the ROI of AI initiatives. This underscores the importance of defining clear metrics tied to tangible outcomes like revenue growth or cost reduction. Metrics that show AI’s value in the real world, not just the theoretical one.
Emerging trends & predictions
As the AI market continues to evolve, it’s crucial to stay ahead of the curve, particularly when it comes to pricing models. In the coming years, you’ll likely see more innovative approaches aimed at increasing accessibility and value.
One trend you should watch is the shift towards performance-based pricing. Vendors like ServiceNow are already testing outcome-based models, where the price you pay is directly linked to measurable business results. This could mean pricing based on customer satisfaction improvements or cost reductions achieved through AI deployment—ultimately aligning the cost with the value delivered.
Another key development, mentioned earlier, is the rise of AI marketplaces. Platforms like Salesforce AppExchange and AWS Marketplace are becoming hubs for ready-to-deploy AI agents, designed to cater to specific industries. These marketplaces reduce development costs by providing businesses with tailored solutions that can be implemented quickly, without the need for extensive customisation. This could save your company both time and money, making AI adoption more accessible.
In addition, tiered subscription models are gaining popularity. These flexible pricing structures are designed to accommodate businesses of all sizes and usage needs. Whether you’re a small business just starting with AI or a large enterprise looking to scale, these models offer a more affordable way to access AI solutions that grow with your organisation.
Your path forward
Navigating the pricing landscape of Agentic AI can feel complex, but the right approach lies in aligning costs with your organisation’s goals and existing technology. Being aware of the current trends will help you make more informed decisions about how to approach AI solutions for your business. While free tools may handle basic tasks, enterprise-grade solutions offer unmatched scalability, security, and customisation that transform how businesses operate. Sirocco specialises in helping organisations like yours make sense of these options, ensuring seamless integration and maximising ROI through tailored solutions. If you’re ready to explore how Agentic AI can elevate your business, book some time with our experts:
